Applying logistic regression for classification in single-channel SSVEP-based BCIs

Steady-State Visual Evoked Potentials (SSVEPs) are electroencephalography (EEG) signals which, recently, have attracted a notable interest in the field of Brain Computer Interfaces (BCIs) due to their little training requirement. Similar to other EEG signals, SSVEPs are captured by means of EEG devices characterized by multiple wet electrodes. Unfortunately, these EEG devices are very uncomfortable for users to be worn. As a consequence, there is a strong interest in developing more comfortable single-channel EEG devices equipped with dry sensors. However, if, on one hand, these innovative EEG devices are less invasive and more simple to be used by an average user, on the other hand, the exploitation of a single dry sensor leads to the collection of a weaker and noisy signal which is more difficult to be opportunely processed and classified. The aim of this paper is to improve the performance of signal classification tasks of SSVEPs captured by single-channel EEG devices with dry sensors by logistic regression. As shown by experimental results, the proposed approach improves the state-of-the-art methods in terms of accuracy.